Soiling is the accumulation of dirt in solar panels which leads to a decreasing trend in solar energy yield and may be the cause of vast revenue losses. The effect of soiling can be reduced by washing the panels, which is, however, a procedure of non-negligible cost. Moreover, soiling monitoring systems are often unreliable or very costly. We study the problem of estimating the soiling ratio in photo-voltaic (PV) modules, i.e., the ratio of the real power output to the power output that would be produced if solar panels were clean. A key advantage of our algorithms is that they estimate soiling, without needing to train on labelled data, i.e., periods of explicitly monitoring the soiling in each park, and without relying on generic analytical formulas which do not take into account the peculiarities of each installation. We consider as input a time series comprising a minimum set of measurements, that are available to most PV park operators. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.

翻译：污秽是太阳能电池板上积累的灰尘，会导致太阳能发电量呈下降趋势，并可能造成巨大的收入损失。清洗电池板可减轻污秽影响，但这一过程成本不容忽视。此外，污秽监测系统往往可靠性差或成本高昂。本研究探讨光伏组件中污秽比率的估算问题，即实际发电功率与假设太阳能电池板清洁时发电功率的比值。我们算法的关键优势在于：无需使用标注数据（即无需在每个电站明确监测污秽时段）进行训练，也无需依赖忽略各安装场所特性的通用分析公式，即可估算污秽程度。我们考虑将包含最小测量数据集的时间序列作为输入，这些数据对大多数光伏电站运营商均可获取。实验评估表明，我们的方法在估算污秽比率方面显著优于当前最先进技术。